As expected, it could be found that the extra process during graph building increased the computational cost.
For MOF data, the average processing time increased from 335.89 milliseconds to 493.15 milliseconds, and from 113.06 milliseconds to 120.7 milliseconds.
It is noticeable that the computational cost of training (forward and backward) reduced significantly.
The reason is that the method in [1]} needs to sum up the effect of the neighbor atoms and multiple convolutions are needed.
Improvement could be made by introducing more computation units and compute the convolutions in parallel.
But the possible improvement is by no means without limitations. Comparingly, the proposed method improves the overall computational efficiency by reducing the training time.
